Abstract

Computation in the mobile grid nodes under the cellular network environment requires an efficient management of wireless channels along with the user mobility. Due to random movement of the mobile devices, the load over the cells in terms of initiation of new computation or carryover computation (handoff) may vary dynamically. This may result in non-availability of free channels for Inter-task communication leading in the drop of carryover computation or to initiate the new computation in the current cell. The proposed work designs a model by instigating substantive cooperation among underutilized and the overloaded cells, considering importance to the frequency reuse and assigning priority to the on-going computation in the computational mobile grid. The model seeks cooperation by grouping the cells in different sizes to reduce the blocking and dropping of the computation. Blocking of the communication is very serious in computational mobile grid environment as the drop may result in the termination of the computation. The model aims at minimizing the Call Block Probability (CBP) and Call Drop Probability (CDP) in the mobile grid by making the clusters of different sizes. A simulation experiment to evaluate the performance of the proposed model reveals the effectiveness of this model.

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1. Introduction

Computational grids are well thought-out in the framework of sharing processing power of many computing devices interconnected by a wired network, in which most of the resources are static in nature. With the advent of the new compute capable mobile devices, such as laptop PCs and PDA’s led to the extension of the idea of compute capable resource sharing in mobile and wireless environment.

The concept of Mobile grid is conceived by combining two technologies viz. Mobile Computing and Grid Computing. Therefore, the issues and the challenges put forth by both; the computational grids and the wireless transmissions are involved in addressing mobile computational grid. Normally, the wireless devices are constrained by limited resource capacity, thus resource availability and high mobility are to be taken into account while focusing on resource sharing among all the static and mobile devices.

Millions of compute capable mobile devices remain unused most of the time. This huge computing warehouse can be potentially utilized to bring forth the concept of mobile grid environment (Kurkovski, Bhagyavati, Ray, & Yang, 2004). There are wide range of heterogeneous and geographically distributed resources in grid, from single processor to multiprocessors, shared memory to distributed memory machines, workstations, sensors and palmtops PDA’s, etc., apart from the software resources. These resources are with different capabilities and configurations and are managed in multiple administrative domains with varying policies.

The concept of the mobile computational grid offers potential processing power which is simply harnessed from the existing hardware technology and unused CPU cycles of the mobile devices. In any typical grid-based problem solving environment, simply by redistributing the computational load of a given problem, it extends the capabilities of solving more complex and resource-demanding problems of the users of mobile devices that otherwise would not have been possible to solve using their individual devices on a stand-alone basis (Chu & Humphrey, 2004; Katsaros & Polyzos, 2007).

A framework of the computational mobile grid, in which the mobile devices that are compute capable and efficient, will make the processing power hub. A prototype is to be building up of a grid-based problem-solving environment for wireless mobile devices with limited processing power. Its primary purpose is to allow mobile devices with limited resources to solve problems that they would not be able to solve individually. This goal is achieved by partitioning and redistributing the computational load among many computing devices as discussed in Vidyarthi (2008).

Advances in wireless network technologies offer the backbone for mobile computing. Wireless communication equips users with the ability to keep talking while on move. Recent past has witnessed the tremendous growth in the use of cellular communication which includes wide range of services from voice call to various other services such as messaging services, e.g., SMS, MMS, chat, email, short range communications like infrared, Bluetooth for data transfer, gaming, photography, bill payments, ticket reservations, application development, etc. Mobile devices viz. cellular phones allow a person to make use of these different services even while travelling because of the supported infrastructure called a cellular network, which integrates cellular devices into the public switched telephone network. Developments in Cellular network technologies lay the foundation for mobile computing.

Computational mobile grid is the assimilation of the mobile devices that are compute capable. These devices may form a grid and cooperate towards execution of an application which may not be possible standalone. Such application, during execution may use various mobile devices in the grid. The part of the application may communicate with each other (Inter-task communication) lying on different mobile devices (Vidyarthi, 2008). In such situation, communication infrastructure of the underlying network should be very efficient to sustain with. The work here, proposes a cooperative cell model that can efficiently support such communication for the computational mobile grid.